Concerning that the increasement of accumulated error causes serious distortion of Unmanned Aerial Vehicle (UAV) remote sensing images stitching, a projection error correction algorithm based on space intersection was proposed, Using space intersection theory, the spatial coordinates of 3D points were calculated according to correspondence points. Then all 3D points were orthographic projected onto the same space plane, and the orthographic points were projected onto the image plane to get corrected correspondence points, Finally, M-estimator Sample Consensus (MSAC) algorithm was used to estimate the homography matrix, then the stitching image was obtained. The simulation results show that this algorithm can effectively eliminate the projection error, thus achieve the purpose of inhibiting UAV remote sensing image stitching error.

In the process of converter blowing state recognition based on flame image recognition, flame color texture information is underutilized and state recognition rate still needs to be improved in the existing methods. To deal with this problem, a new converter blowing recognition method based on feature of flame color texture complexity was proposed. Firstly, the flame image was transformed into HSI color space, and was nonuniformly quantified; secondly, the co-occurrence matrix of H component and S component was computed in order to fuse color information of flame images; thirdly, the feature descriptor of flame texture complexity was calculated using color co-occurrence matrix; finally, the Canberra distance was used as similarity criteria to classify and identify blowing state. The experimental results show that in the premise of real-time requirements, the recognition rate of the proposed method is increased by 28.33% and 3.33% respectively, compared with the methods of Gray-level co-occurrence matrix and gray differential statistics.